Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

The use of ChatGPT to find similar institutions for institutional benchmarking

Scientometrics(2024)10.1007/s11192-024-05039-7Source: DataRank Database

The use of ChatGPT to find similar institutions for institutional benchmarking is a research paper published in Scientometrics (2024). On theSindex it has a DataRank of 0.385. It has been cited 12 times.

N/A

0.385DataRank · unranked

0.385

Open Access12 citations · base score 2.6

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

AbstractIn evaluative bibliometrics and higher education studies, one is frequently confronted with the task of comparing institutions with similar institutions. In this Letter to the Editor, a simple approach is discussed which applies ChatGPT. Although the approach seems to produce promising results (tested with an example at the level of research institute and of a university), it is necessary to investigate it systematically based on a sample including many institutions before it should be applied in research evaluation. The challenge in systematic investigations is that ChatGPT provides the user with different answers on the sane request (missing reliability).

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.385

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

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Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

Read the full methodology →